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Sequential Pattern Discovery for Weather Prediction Problem

机译：天气预报问题的顺序模式发现

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This study proposes the Sequential Pattern Discovery algorithms to solve weather prediction problem. A novel weather pattern discovery framework is presented to highlight the important processes in this work. Two algorithms are employed; namely episodes and sequential pattern mining algorithms. The episodes mining algorithm is introduced to find frequent episodes in rainfall sequences and sequential pattern mining algorithm to find relationship of patterns between weather stations. Real data are collected from ten rainfall stations of Selangor State, Malaysia. The sequential pattern algorithm is applied to extract the relationship between ten rainfall stations in 33 years periods of time. The patterns are evaluated experimentally by support and confidence values while some specific rules are mapped to the location of stations and analysed for more verification. The proposed study produces valuable patterns of weather and preserves important knowledge for weather prediction.
机译：这项研究提出了顺序模式发现算法来解决天气预报问题。提出了一种新颖的天气模式发现框架，以突出显示这项工作中的重要过程。采用两种算法;即情节和顺序模式挖掘算法。引入了情节挖掘算法来发现降雨序列中的频繁情节，并引入顺序模式挖掘算法来找到气象站之间的模式关系。实际数据是从马来西亚雪兰莪州的十个降雨站收集的。应用顺序模式算法提取33年内十个降雨站之间的关系。通过支持和置信度值对模式进行实验评估，同时将一些特定规则映射到站点位置，并进行分析以进行更多验证。拟议的研究产生了有价值的天气模式，并保留了有关天气预报的重要知识。

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